Predicting churn for (mobile) app usage

Abstract

A churn prediction model is presented that uses both behavioral data as well as user characteristics to predict whether a given user will churn (i.e., stop using) an application. Initially a training set of user interactions can be correlated to a churn probability value for various sequences of user activity. Then, as regards a real time user, user actions in navigating through the app may be recorded, and this information can be used, in addition to user characteristics, to predict the probability that this user will churn, thus implementing in a “nip churn in the bud” approach (or, the inverse, remain loyal and continue to use the app). In some embodiments, a partial set of user actions can be identified as subsequences of known churn sequences. To users performing those subsequences of activity, a real time message, offer or promotion may be sent so as to influence them not to churn. In exemplary embodiments of the present invention, user data may be uploaded from a user's device to proprietary or cloud servers. Churn analysis, or a more detailed churn analysis, using up to the minute collective data for the given app, may, for example, be performed on those servers.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]The present application claims the benefit of U.S. Provisional Patent Application No. 62 / 265,552, filed on Dec. 10, 2015, the disclosure of which is hereby incorporated herein by reference as if fully set forth.FIELD OF THE INVENTION[0002]The present invention generally relates to personal electronic devices, such as smartphones, tablets and computers, and in particular to metrics and models for predicting what percentage of the population that tries a given application on a user device will continue using it at various subsequent time periods, and with what regularity.BACKGROUND OF THE INVENTION[0003]This application relates generally to application software, commonly referred to as an “app.” Apps are computer software designed to help the user to perform specific tasks. Apps may be executed on a variety of computing devices, such as on mobile devices including smartphones. For example, mobile apps are software applications designed to r...

AI Technical Summary

Benefits of technology

This patent discusses a way to predict when users will stop using an application. The model looks at user behavior and characteristics to determine the likelihood of churn (when a user stops using an application). The system records user interactions and uses them, along with other data, to make predictions in real-time. Users may receive messages or promotions to encourage them to stay with the application. This information is collected and analyzed on servers, which can predict churn events and make recommendations to reduce the likelihood of churn.

Problems solved by technology

However, these categories are simply too broad to granularly predict how a user of an app seems to like using it, and whether he or she will continue to use it, continue to experiment with it, or decide that it is not useful to him or her.

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